For an automotive pedestrian detection radar system, fast-ramp based 2D range-Doppler Frequency Modulated Continuous Wave (FMCW) radar is effective for distinguishing between moving targets and unwanted clutter. However, when a weak moving target such as a pedestrian exists together with strong clutter, the pedestrian may be masked by the side-lobe of the clutter even though they are notably separated in the Doppler dimension. To prevent this problem, one popular solution is the use of a windowing scheme with a weighting function. However, this method leads to a spread spectrum, so the pedestrian with weak signal power and slow Doppler may also be masked by the main-lobe of clutter. With a fast-ramp based FMCW radar, if the target is moving, the complex spectrum of the range- Fast Fourier Transform (FFT) is changed with a constant phase difference over ramps. In contrast, the clutter exhibits constant phase irrespective of the ramps. Based on this fact, in this paper we propose a pedestrian detection for highly cluttered environments using a coherent phase difference method. By detecting the coherent phase difference from the complex spectrum of the range-FFT, we first extract the range profile of the moving pedestrians. Then, through the Doppler FFT, we obtain the 2D range-Doppler map for only the pedestrian. To test the proposed detection scheme, we have developed a real-time data logging system with a 24 GHz FMCW transceiver. In laboratory tests, we verified that the signal processing results from the proposed method were much better than those expected from the conventional 2D FFT-based detection method.

We designed and developed a 24 GHz surveillance FMCW (Frequency Modulated Continuous Wave) radar with a softwarereconfigurable baseband. The developed radar system consists of transceiver, two selectable transmit antennas, eight parallel receive antennas, and a back-end module for data logging and to control the transceiver. The architecture of the developed radar system can support various waveforms, gain control of receive amplifiers, and allow the selection of two transmit antennas. To do this, we implemented the transceiver using a frequency synthesizer device and a two-step VGA (Variable Gain Amplifier) along with switch-controlled transmit antennas. To support high speed implementation features along with good flexibility, we developed a back-end module based on a FPGA (Field Programmable Gate Array) with a parallel architecture for the real-time data logging of the beat signals received from a multichannel 24 GHz transceiver. To verify the feasibility of the developed radar system, signal processing algorithms were implemented on a host PC. All measurements were carried out in an anechoic chamber to extract a 3D range-Doppler-angle map and target detections. We expect that the developed software-reconfigurable radar system will be useful in various surveillance applications.

FMCW(Frequency Modulation Continuous Wave) radar has many useful applications but a serious problems can occur in multi-target situations. Range-velocity processing should suppress so-called ghost targets and detect missing targets presented by beat frequency shift with Doppler frequency. In this paper, a new method is proposed for effective identification of the correct pairs of beat frequencies received from real targets.

In this paper, we propose a Doppler-spectrum feature-based human–vehicle classification scheme for an FMCW (frequency-modulated continuous wave) radar sensor. We introduce three novel features referred to as the scattering point count, scattering point difference, and magnitude difference rate features based on the characteristics of the Doppler spectrum in two successive frames. We also use an SVM (support vector machine) and BDT (binary decision tree) for training and validation of the three aforementioned features. We measured the signals using a 24-GHz FMCW radar front-end module and a real-time data acquisition module and extracted three features from a walking human and a moving vehicle in the field. We then repeatedly measured the classification decision rate of the proposed algorithm using the SVM and BDT, finding that the average performance exceeded 99% and 96% for the walking human and the moving vehicle, respectively.